store = {}
store['args']={'name': 'emnist_independent_bald_k10_629535', 'available_sample_k': 5, 'num_inference_samples': 10, 'seed': 629535, 'acquisition_method': 'AcquisitionMethod.independent', 'experiment_description': 'EMNIST with b5 and k10, k100 with both BALD and BatchBALD', 'type': 'AcquisitionFunction.bald', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 0, 'experiments_laaos': './experiment_configs/emnist_bbb/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=0', '--experiments_laaos=./experiment_configs/emnist_bbb/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.02303191489361702, 'nll': 3.874142925587106}, 'chosen_samples': [52868, 110202, 80959, 26801, 92372], 'chosen_samples_score': [0.02973660677671397, 0.02984544374048692, 0.030028121173381717, 0.030705981701612206, 0.03017405793070793], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.03792553191489362, 'nll': 63.46773865395404}, 'chosen_samples': [91340, 92885, 36324, 106698, 19843], 'chosen_samples_score': [1.1195324539173954, 1.1225932348058754, 1.133924948123337, 1.1828941089942753, 1.143458710401667], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.06696808510638298, 'nll': 43.015435365717465}, 'chosen_samples': [88826, 17786, 80293, 67530, 90032], 'chosen_samples_score': [1.302865093022106, 1.3217885977486232, 1.3233135322281422, 1.3339999518969254, 1.4234378273269037], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.06175531914893617, 'nll': 47.46925924422893}, 'chosen_samples': [60427, 65043, 36974, 30809, 17948], 'chosen_samples_score': [1.4282620936515897, 1.4361263231338146, 1.4567923592586538, 1.4590841913172916, 1.489271543880013], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.09824468085106383, 'nll': 34.50870575925137}, 'chosen_samples': [103484, 2128, 38234, 42337, 103678], 'chosen_samples_score': [1.3936020625699812, 1.4033211872267741, 1.4559713726228125, 1.482420725740702, 1.460611481295976], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.11728723404255319, 'nll': 33.671731457000085}, 'chosen_samples': [23874, 91255, 67569, 84602, 23647], 'chosen_samples_score': [1.5088878830708006, 1.5275863427263083, 1.5137225729955026, 1.5317142767419551, 1.624564073455915], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.11478723404255319, 'nll': 39.17968548064536}, 'chosen_samples': [53178, 38505, 25089, 98971, 13524], 'chosen_samples_score': [1.6156083589653822, 1.6180536462703285, 1.6517301140525116, 1.6772750769529776, 1.682556296112328], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.12414893617021276, 'nll': 38.32761011326566}, 'chosen_samples': [49735, 68258, 15818, 34742, 98394], 'chosen_samples_score': [1.6297485415208222, 1.6324079308329122, 1.634362863948777, 1.6853567947480312, 1.6441739835034939], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.14409574468085107, 'nll': 36.22260513305664}, 'chosen_samples': [39674, 58542, 52914, 10228, 43917], 'chosen_samples_score': [1.4815728583256773, 1.4821678451122418, 1.5111868486097968, 1.5033929443187999, 1.4956725797775126], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.16069148936170213, 'nll': 32.152530264347156}, 'chosen_samples': [65945, 4757, 86696, 26689, 11916], 'chosen_samples_score': [1.595299573918195, 1.6082443216232882, 1.616860259265466, 1.657354515606318, 1.6190507648017967], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.16632978723404254, 'nll': 28.580476751124607}, 'chosen_samples': [87308, 31602, 105543, 45994, 48868], 'chosen_samples_score': [1.5330182048040721, 1.537264307453417, 1.5411120153367523, 1.55561009964741, 1.5932900891863517], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.16680851063829788, 'nll': 24.13664807096441}, 'chosen_samples': [64831, 49137, 99801, 3395, 22442], 'chosen_samples_score': [1.5571155758344402, 1.5653103453864725, 1.5839109011439576, 1.5805327902454573, 1.595132665737757], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.17127659574468085, 'nll': 24.54644966937126}, 'chosen_samples': [34746, 80234, 93508, 22956, 110441], 'chosen_samples_score': [1.5209312911031612, 1.5318637013832441, 1.5523564558002032, 1.5453532307127578, 1.554266232880792], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.195, 'nll': 25.354078448681122}, 'chosen_samples': [50448, 83728, 2151, 105909, 43859], 'chosen_samples_score': [1.6368837001034737, 1.6497672952210893, 1.7264414700174642, 1.6568367912839976, 1.6666125829677365], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.2123404255319149, 'nll': 22.468138869265292}, 'chosen_samples': [72215, 1296, 73676, 54685, 97951], 'chosen_samples_score': [1.5667549496566173, 1.5702447297758486, 1.5709762771395945, 1.5980301672726065, 1.6189983144223494], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.21579787234042552, 'nll': 22.230799855983005}, 'chosen_samples': [69357, 96448, 45243, 12146, 102836], 'chosen_samples_score': [1.5847291090530002, 1.5972871329491167, 1.6115060261322245, 1.6245873294292634, 1.6531991895658933], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.214468085106383, 'nll': 21.491738342934468}, 'chosen_samples': [11707, 30284, 6206, 25489, 37937], 'chosen_samples_score': [1.6879620332686964, 1.7230757379922053, 1.75267740901586, 1.69080398300624, 1.7571914280557968], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.22648936170212766, 'nll': 20.20318122052132}, 'chosen_samples': [14946, 80127, 67702, 63713, 40120], 'chosen_samples_score': [1.7245261702284804, 1.7465501274419424, 1.738525104204603, 1.7323723049242257, 1.7844099440879297], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.22595744680851063, 'nll': 19.629085860556746}, 'chosen_samples': [69861, 22724, 107091, 88129, 12644], 'chosen_samples_score': [1.6965547156234457, 1.707634699634456, 1.8274163199140805, 1.7950910529344468, 1.7346183930371808], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.21324468085106382, 'nll': 17.34308235817767}, 'chosen_samples': [34853, 48513, 66298, 34265, 106636], 'chosen_samples_score': [1.5592755331949917, 1.5663407456580938, 1.6098116374896057, 1.5985495576388176, 1.5925112364536669], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.22, 'nll': 16.979832723089988}, 'chosen_samples': [11449, 89984, 39938, 28737, 16278], 'chosen_samples_score': [1.5160187440991924, 1.6020434581482839, 1.5617893747241474, 1.7413985846382365, 1.6232377614759335], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.23090425531914893, 'nll': 16.624389297810005}, 'chosen_samples': [37354, 47008, 46561, 70452, 52094], 'chosen_samples_score': [1.7077538530702348, 1.7078644825598444, 1.781107843770529, 1.7096955334023338, 1.756889881002776], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.25718085106382976, 'nll': 15.665407827661392}, 'chosen_samples': [62025, 83019, 73267, 83041, 102997], 'chosen_samples_score': [1.6306219910363189, 1.6361590152269492, 1.6461520984561953, 1.6859624487610911, 1.6720198830637818], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.27122340425531916, 'nll': 16.55025547433407}, 'chosen_samples': [92525, 28759, 78237, 76853, 47670], 'chosen_samples_score': [1.646129726707473, 1.699805038045852, 1.6586814472735156, 1.7093487284724798, 1.6494789455513965], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26867021276595743, 'nll': 14.79776384962366}, 'chosen_samples': [97013, 95898, 5859, 16583, 75309], 'chosen_samples_score': [1.6632326604125396, 1.7233773734684235, 1.7589439083773968, 1.6632742891399372, 1.668414925366494], 'chosen_samples_orignal_score': None})
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